As science and technology of our society continue to progress, three dimensional (3D) technologies have become an important component of modern science and technology. The 3D model triangular facet feature learning and classifying technology, which is one of the fundamental techniques in 3D model interpretation and processing, plays a huge role in 3D modeling, 3D animation, 3D mapping and many other 3D technology fields.
In prior arts, various 3D model triangular facet feature learning and classifying techniques have been proposed. For example, Zhenyu Shu et al. of Zhejiang University proposed an unsupervised and deep learning based method for classifying and co-segmenting 3D model triangular facets in 2016. The method involves extracting 3D model features on the basis of pre-segmentation, reconstructing and learning the features using a deep learning model auto-encoder under the condition of non-supervision, and obtaining classifying and co-segmenting result for the 3D model triangular facets by clustering the output features using a Gaussian mixture model (GMM). Yet, by employing an unsupervised feature learning manner, said method fails to guarantee that correct output feature is obtained via the learning, nor that accurate triangular facet classifying result is obtained.
In view of the above, features extracted in prior art have insufficient capability to describe the triangular facets, leading to inaccurate results of 3D model triangular facet feature learning and classifying.